Method and system for dynamic feature detection

ABSTRACT

Disclosed are methods and systems for dynamic feature detection of physical features of objects in the field of view of a sensor. Dynamic feature detection substantially reduces the effects of accidental alignment of physical features with the pixel grid of a digital image by using the relative motion of objects or material in and/or through the field of view to capture and process a plurality of images that correspond to a plurality of alignments. Estimates of the position, weight, and other attributes of a feature are based on an analysis of the appearance of the feature as it moves in the field of view and appears at a plurality of pixel grid alignments. The resulting reliability and accuracy is superior to prior art static feature detection systems and methods.

FIELD OF THE INVENTION

The invention relates to digital image analysis, and more particularlyto feature detection and its application to optical measurement ofmotion.

BACKGROUND OF THE INVENTION

Digital image analysis is used for many practical purposes including,industrial automation, consumer electronics, medical diagnosis,satellite imaging, photographic processing, traffic monitoring,security, etc. In industrial automation, for example, machine visionsystems use digital image analysis for automated product inspection,robot guidance, and part identification applications. In consumerelectronics, for example, the common optical mouse uses digital imageanalysis to allow a human to control a cursor on a personal computerscreen.

To service these applications, digital images are captured by a sensor,such as an optoelectronic array of photosensitive elements calledpixels, and analyzed by a digital information processing device, such asa digital signal processor (DSP) or a general purpose computer executingimage analysis software, to extract useful information. One common formof digital image analysis is feature detection. Physical features onobjects in the field of view of the sensor (object features) give riseto patterns in the digital images (image features) that can be analyzedto provide information about those object features and the objects thatcontain them. Example object features might include edges, corners,holes, ridges, and valleys, which give rise to changes in surface depth,orientation, and reflectance. These changes in turn interact withilluminating radiation to produce the image features.

Image features can be detected using many well-known methods, forexample edge detection, matched filters, connectivity, Hough transforms,and geometric pattern matching.

Typically, feature detection in digital image analysis is a staticprocess, by which is meant, generally, that features are detected withina single digital image captured at a particular point in time.Equivalently, to reduce noise, static feature detection can be used onan image that is the average of a plurality of images captured from astationary scene.

In a typical static feature detection system, a one- or two-dimensionaldigital image of a scene is captured by any suitable means. The image isthen analyzed by software implementing a static feature detectiontechnique to identify image features, which comprise a set of attributesthat represent measurements of physical properties of correspondingobject features. In a two-dimensional edge detection system, forexample, edge attributes may comprise a position, an orientation and aweight. The position estimates the location of the edge within theimage, and may be determined to sub-pixel precision by well-known means.The orientation estimates the angle of the edge at the estimatedposition. The weight is an estimate of edge strength and can be used toprovide a measure of confidence that the edge truly corresponds to aphysical feature in the field of view and not to some artifact ofinstrumentation. Typically, an edge is considered to exist only if itsweight exceeds some value herein called a detection threshold.

In a one-dimensional edge detection system, for example, position andweight can be similarly estimated and generally have the same meaning,but orientation is replaced with polarity, which is a two-state (binary)value that indicates whether the edge is a light-to-dark ordark-to-light transition.

There are a large number of well-known static edge detection systems,including those of Sobel and Canny. Another exemplary 2D static edgedetection technique is described in U.S. Pat. No. 6,690,842, entitledAPPARATUS AND METHOD FOR DETECTION AND SUB-PIXEL LOCATION OF EDGES IN ADIGITAL IMAGE, by William Silver, the contents of which are herebyincorporated by reference. Generally the literature describestwo-dimensional methods, with one-dimensional being a special andsimpler case. Static edge detection techniques may utilize gradientestimation, peak detection, zero-crossing detection, sub-pixelinterpolation, and other techniques that are well known in the art.

Another example of static feature detection is the Hough transform,described in U.S. Pat. No. 3,069,654 entitled METHOD AND MEANS FORRECOGNIZING COMPLEX PATTERNS, and subsequently generalized by others.For a Hough transform designed to detect lines in a 2D image, forexample, the feature attributes might include position and orientation.

Yet another example of static feature detection is connectivityanalysis, where feature attributes might include center of mass, area,and orientation of the principal axes.

All of the information estimated by a static feature detection techniqueis limited in accuracy and reliability by the resolution and geometry ofthe pixel grid. This is because the exact alignment between the pixelgrid and the physical features that give rise to image features isessentially an accident of the process by which objects or material arepositioned in the field of view at the time that an image is captured.Edge weight, for example, varies significantly depending on thisaccidental alignment, which can result in failing to detect a true edgeor falsely detecting an instrumentation artifact. This is particularlylikely for edges at the limits of the resolution of the pixelgrid—detection of such edges, whether real or artificial, is at the whimof their accidental alignment with the pixel grid.

Position estimates are subject to the same whims of accidentalalignment. A competent static edge detector might estimate the positionof a strong, well-resolved edge to about ¼ pixel, but it is difficult todo much better. For weaker or inadequately-resolved edges, the accuracycan be substantially worse.

Static feature detection is used in the common optical mouse to trackthe motion of the mouse across a work surface. Methods in common use aredescribed in, for example, U.S. Pat. Nos. 5,578,813, entitled FREEHANDIMAGE SCANNING DEVICE WHICH COMPENSATES FOR NON-LINEAR MOVEMENT,5,644,139, entitled NAVIGATION TECHNIQUE FOR DETECTING MOVEMENT OFNAVIGATION SENSORS RELATIVE TO AN OBJECT, 5,786,804, entitled METHOD ANDSYSTEM FOR TRACKING ATTITUDE, and 6,433,780, entitled SEEING EYE MOUSEFOR A COMPUTER SYSTEM. A reference pattern is stored corresponding tophysical features on the work surface, where the reference pattern is aportion of a digital image of the surface. The reference pattern iscorrelated with subsequent digital images of the surface to estimatemotion, typically using sum of absolute differences for the correlation.Once motion exceeding a certain magnitude is detected, a new referencepattern is stored. This is necessary because the old reference patternwill soon move out of the field of view of the sensor.

Correlation of this sort is a form a static feature detection. Theposition of the mouse when a new reference pattern is stored is anaccidental alignment of the physical features of the surface with thepixel grid of the sensor, and each time a new reference pattern isstored there will be some error. These errors accumulate proportional tothe square root of the number of times a new reference pattern isstored, and quickly become rather large. This is generally not a seriousproblem for an optical mouse, because a human user serves as feedbackloop controlling the motion to achieve a desired effect.

There are a large number of applications for which accurate tracking ofthe motion of objects or material is of considerable practical value,and where traditional methods of digital image analysis, such as thatused in an optical mouse, are inadequate. These applications includenumerous examples in industrial manufacturing, including for example thetracking of discrete objects for control of an ink jet or laser printer,and the tracking of material in a continuous web. The most commonly usedsolution is a mechanical encoder attached to a transport drive shaft,but these have many well-known problems including slip between the driveand the material, resulting in inaccuracies. Systems using laser Dopplertechnology for direct non-contact measurement of surface speed areavailable, but they are generally expensive and bulky.

SUMMARY OF THE INVENTION

The present invention overcomes the disadvantages of the prior art byproviding a system and method for dynamic feature detection. Dynamicfeature detection substantially reduces the effects of accidentalalignment of physical features with the pixel grid by using the motionof objects or material in and/or through the field of view to captureand process a plurality of images that correspond to a plurality ofalignments. Estimates of the position, weight, and other attributes of afeature are based on an analysis of the appearance of the feature as itmoves in the field of view and appears at a plurality of pixel gridalignments. The resulting reliability and accuracy is superior to priorart static feature detection systems and methods.

According to the present invention a dynamic feature comprisesinformation that describes and corresponds to a physical feature orcharacteristic of an object or material (an object feature), wherein theinformation is determined by analysis of a plurality of images capturedduring relative movement between the object or material and the field ofview. The information comprises at least one measurement of a physicalproperty of the object feature, for example its position. Individualelements of the information are called attributes or values. In anillustrative embodiment using edges, the information may includeposition, orientation (2D) or polarity (1D), and weight, each similar tobut more reliable and accurate than those of prior art static edgedetection. In alternative embodiments, the information may furtherinclude other values unanticipated by static feature detection, such asage and variability. In an illustrative embodiment, age is a count ofthe number of images for which the dynamic feature has appeared in thefield of view. Variability attributes are measures of the magnitude offluctuation of other attributes such as position, weight, and/ororientation, and serve to provide assessments of the reliability ofthose attributes.

As used herein the term static feature refers to an image featuredetected by some suitable static feature detection method or system. Theterms static edge and dynamic edge refer respectively to static ordynamic features in an embodiment wherein the features to be detectedare edges. In descriptions of such embodiments it will be apparent thatother types of features could be used, possibly with differentattributes that are more suitable for the type of features chosen.

Motion is a nearly universal characteristic of objects and material inthe physical world. Even for objects that come to rest within the fieldof view of a sensor, the object must have moved in order to reach thatpoint of rest. In industrial manufacturing, for example, objects andmaterial generally move in a known direction along a production line.

The invention takes advantage of the fact that the apparent motion of afeature from one image to the next is generally composed of twoprincipal factors: the physical motion of an object or material in thefield of view, and instrumentation artifacts such as accidentalalignment with the discrete pixel grid, varying illumination and viewingangles, and others known in the art. The physical component of motion isgenerally substantially common to all features belonging to the sameobject, whereas the artificial components are generally substantiallyuncorrelated. The invention provides image analysis systems and methodsfor estimating the physical component of motion wherein theinstrumentation artifacts substantially cancel out, resulting inestimates of the physical motion that are far more accurate than theestimates of attributes, such as position and orientation, of individualstatic features.

According to an illustrative embodiment of the present invention,physical motion is described by one or more parameters corresponding toone or more degrees of freedom in which the motion may occur. Forexample, motion of rigid bodies in a 2D image might be described bythree parameters, two of translation and one of rotation. Similarly,motion of a rigid body in a 1D image would be described by onetranslation parameter. Motion closer to and/or farther away from certainkinds of sensors can be described by a size degree of freedom, since theobject would appear larger or smaller as it moved closer or farther. Fora 1D sensor of this kind, for example, motion could be described by twoparameters, one of translation and one of size.

In general many other well-known degrees of freedom can be used asappropriate to the motion in a given application, including, forexample, those of the affine and perspective transformations.Furthermore, the objects need not be rigid bodies as long as theparameters of motion can be defined. The set of parameters used todefine the degrees of freedom in a particular embodiment are referred tocollectively as the pose of the object or material relative to the fieldof view, or conversely the pose of the field of view relative to theobject or material. The pose provides, for each image, a mapping betweenpoints on the object and points in the image.

Certain kinds of motion, such as for example rotation, suggests the useof 2D images. The invention recognizes, however, that embodimentswherein the motion is suitable for 1D images are of considerable andspecial practical value, and that 1D digital image analysis has beendisfavored relative to 2D devices. The practical value arises in partbecause motion in industrial manufacturing is generally characterized byone degree of freedom along a production line, and in part because 1Dsystems are capable of much finer time resolution than 2D systems.

Due to its practical importance and also for simplicity of description,the disclosure herein emphasizes embodiments using 1D images with onetranslation degree of freedom. It will be recognized, however, thatembodiments using 2D images, or 1D images with different or additionaldegrees of freedom, are within the scope of the present invention.Further due to its practical importance and also for simplicity ofdescription, the disclosure herein emphasizes embodiments wherein thefeatures to be detected are edges. It will further be recognized,however, that embodiments based on other kinds of features are withinthe scope of the present invention.

In some embodiments the objects or material in the field of view are ofsubstantially unknown appearance, i.e. the object features are not knownin advance. Dynamic features are created as motion brings new physicalfeatures or characteristics into the field of view, are updatedresponsive to a learning process over a plurality of images as theobjects or material move; and may optionally be discarded once themotion carries them substantially beyond the field of view.

In other embodiments a predetermined set of dynamic features is used,corresponding to objects or material of substantially known appearance.The predetermined set of dynamic features may be obtained from atraining process that is responsive to a training object; from a CAD ormathematical description; or from any suitable process. For suchembodiments, the dynamic feature attributes may include both expectedand measured values. The expected attributes may come from predeterminedinformation, while the measured attributes may be initialized wheneveran object substantially similar in appearance to the known appearanceenters the field of view, and be updated as the object moves. Suchembodiments may further include in the set of dynamic feature attributesage and variability.

While some motion is a necessary element of dynamic feature detection,the motion need not be continuous or smooth. Motion may change inmagnitude and/or direction arbitrarily and without any prior knowledgeor warning. Furthermore, objects may come to rest in the field of viewfor indefinite periods of time. In all cases, motion is understood to berelative motion between a sensor and an object. Illustrative embodimentsof the present invention may be described herein using a stationarysensor and a moving object. It should be noted that in alternativeembodiments, the sensor may be in motion while the object is stationaryand/or both the object and the sensor may be in motion. As such, thedescription of a stationary sensor and a moving object should be takenas exemplary only.

In operation, an exemplary dynamic feature detection system or methodcaptures a sequence of images of a field of view and performs staticfeature detection to identify a set of static features for each capturedimage. The static feature detection may use any suitable method.

An analysis is performed to determine a map between the static anddynamic features for each captured image. The analysis is advantageouslydesigned to bring the static features into a substantially bestalignment with the dynamic features. The map generally has twocomponents: a pose that maps between points on the object and points inthe image, and an association that maps dynamic features tocorresponding static features. Typically a dynamic feature correspondsto one static feature, but there may be ambiguous cases where a dynamicfeature corresponds to more than one static feature, as well as caseswhere a dynamic feature corresponds to no static feature. In addition toproducing a map, the analysis may produce a match score that indicates adegree of confidence that that map is valid, i.e. that the object ormaterial continues to be correctly tracked as it moves.

Using the map, the attributes of dynamic features are updated by alearning process whose details are specific to a given embodiment. Oneeffect of the learning process is to improve the measurements ofphysical properties of corresponding object features, for example bymaking them more accurate or reliable.

For embodiments wherein the objects or material are of substantiallyunknown appearance, dynamic features are created as new physicalfeatures or characteristics enter the field of view, are updated by alearning process as they move, and are optionally discarded after theyleave the field of view. The terms birth, life, and death are usedherein to refer to the processes of creation, updating, and discardingof dynamic features, and these processes are collectively referred to asthe life cycle of a dynamic feature.

In illustrative embodiments dynamic features have an attribute calledherein experience. In some illustrative embodiments experience maysimply be the age of a dynamic feature, which in an illustrativeembodiment starts at 0 at birth and increases by 1 for each capturedimage during its life. In other illustrative embodiments, experience isa combination of age and the weights of static features to which thedynamic feature was found to correspond, so that, for example, dynamicedges corresponding to strong, well-resolved object features tend togain more experience as they get older than do dynamic edgescorresponding to weaker or under-resolved object features.

In illustrative embodiments experience has an important and significanteffect on the learning process. Less experienced dynamic featuresgenerally have relatively less reliable attributes than more experiencedones. As a consequence, it is desirable that less experienced dynamicfeatures contribute relatively less to the analysis that determines themap and match score, but are influenced relatively more by the learningprocess that updates attribute data. Likewise, it is desirable that moreexperienced dynamic features contribute relatively more to the analysisand are influenced relatively less (or not at all) by the learningprocess. The learning process reduces the magnitude of uncorrelatedinstrumentation artifacts, resulting in attributes that are moreaccurate and reliable than can be obtained by prior art static featuredetection.

In these illustrative embodiments, since the map is most stronglyinfluenced by experience, and since it most strongly affects lessexperienced dynamic edges through the learning process, the life cycleof dynamic features has the effect of transferring information from moreexperienced to less experienced dynamic features. A feature is born,learns its attributes from its elders, passes that information along tothe younger generations, and finally dies.

As described above, the analysis that determines the map may compute amatch score for each image. In an illustrative embodiment the matchscore is most strongly influenced by the most experienced dynamicfeatures. In an illustrative embodiment if the match score falls below acertain accept threshold, the map is considered unreliable and the imageis otherwise ignored. A more reliable map may be found with the nextimage. If a certain number of consecutive match scores fall below theaccept threshold, or if one such score falls below a certain flushthreshold, all dynamic features are discarded and new ones are createdfrom the static features of the current image. In accordance withalternative embodiments, many other rules can be used to handlesituations where the map may be unreliable or where tracking of theobject may fail altogether.

In some embodiments for which objects or material of substantially knownappearance are used, the analysis that determines the map may computetwo scores, a match score similar in meaning and use to that describedabove and indicating confidence in the map, and a detect score thatindicates the confidence with which an object similar in appearance tothe known appearance is in the field of view. One advantage of such anembodiment is that it may be desirable to recognize and begin to trackan object when it is only partially within the field of view, beforeenough of it is seen to be sure that it is in fact an object of theknown appearance. At such times it is desirable to have a measure ofconfidence in the map (the match score), but to reserve judgment on theidentity of the object (detect score) until more is seen.

In some embodiments for which objects or material of substantially knownappearance are used, the predetermined set of dynamic features areobtained using a training process that determines those dynamic featuresfrom a training object. Since that predetermined set of dynamic featureswill be used in every image of such an embodiment of dynamic featuredetection, it is desirable that the attributes of each dynamic featurein the set be as reliable as is practical, and that only features thatare seen relatively consistently over various pixel grid alignments beincluded in the set. For example, under-resolved features may appearstrongly at certain pixel grid alignments but not at others, and theirstatic position are generally not reliable.

In an illustrative embodiment, training on a training object isaccomplished by using a dynamic feature detection embodiment designed tooperate on unknown objects or material. As will be appreciated, thetraining object or material remains unknown until such time as itbecomes known by the training process itself. In this embodiment,dynamic feature detection on unknown objects or material is run as thetraining object is moved into the field of view. A signal is received tocomplete the training process by examining the dynamic features inexistence at the time of the signal, choosing those whose attributesindicate reliability, and creating the predetermined set of dynamicfeatures to be used for dynamic feature detection on objectssubstantially similar in appearance to the training object. The trainingobject may be at rest or in motion at the time of the signal.Reliability may be indicated by average weight, position variability, orany other suitable process that will occur to one of ordinary skill inthe art.

One result of the dynamic feature detection systems and methods inaccordance with illustrative embodiments of the present invention is toproduce a sequence of poses, each pose corresponding to an image. Bydividing the change in pose coordinates between two images by the timedifference between the times at which the images were captured,estimates of the velocity of motion of objects or material are made.Velocity can have up to the same number of degrees of freedom aspose—translation degrees of freedom give rise to linear velocityestimates, for example, and orientation degrees of freedom give rise toangular velocity estimates. In an illustrative embodiment, differencesin pose and time are computed between successive images and then theirratios are filtered by a low-pass filter.

The velocity estimates can be used for a variety of purposes. In anillustrative embodiment, the filtered velocity estimates are used topredict the pose of the field of view relative to the object or materialas part of the analysis that determines the map. The predicted pose isused by the analysis to make it run faster, which is desirable so as tokeep the time resolution of the system as fine as is practical.

Dynamic edge detection systems and methods produce a wealth ofinformation, including, for example, the attributes of dynamic features,sequence of poses, and a sequence of velocity estimates. Thisinformation can be used for a wide variety of purposes that will occurto one of ordinary skill.

In illustrative embodiments of particular practical value, the sequenceof pose and/or velocity estimates obtained from unknown objects ormaterials are provided to external equipment by means of suitablesignals. Signals may include, for example, the well-known quadraturesignals, analog current loop, and/or any serial or parallelcommunications protocol. These illustrative embodiments can be used toadvantage in, for example, industrial manufacturing applications as analternative to conventional mechanical shaft encoders, for discreteobjects, continuous webs of material, or any other object or materialwhere dynamic features can be detected. In such applications theconventional shaft encoders provide reliable measurement of the rotationof transport drive shafts, but not necessarily the position and/orvelocity of the actual object or material being transported. The presentinvention provides a practical, inexpensive, non-contact, and/orreliable means for directly tracking the motion of the objects ormaterial.

In another illustrative embodiment of particular practical value,dynamic feature detection using objects of substantially knownappearance is used in an improved method and system for optoelectronicdetection and location of objects. Methods and systems for such apurpose were disclosed in U.S. patent application Ser. No. 10/865,155,entitled METHOD AND APPARATUS FOR VISUAL DETECTION AND INSPECTION OFOBJECTS, and U.S. patent application Ser. No. 11/763,752, entitledMETHOD AND SYSTEM FOR OPTOELECTRONIC DETECTION AND LOCATION OF OBJECTS,the contents of both of which are hereby incorporated herein byreference. In certain illustrative embodiments disclosed in theseapplications, various systems and methods were taught to locate objectsof substantially known appearance. Systems and methods of the presentinvention, or portion thereof, can be used in combination or as analternative to provide improved accuracy, reliability, and/or ease oftraining among other benefits.

The above-incorporated U.S. patent application Ser. No. 11/763,752,while not teaching or anticipating dynamic feature detection, doesdisclose an apparatus and process for one-dimensional image capture thatcan be used advantageously to practice certain 1D embodiments of thepresent invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be understood from the following detaileddescription, along with the accompanying figures, wherein:

FIG. 1 shows an example application of a system according to theinvention for detecting discrete objects of substantially knownappearance, and/or tracking discrete objects of substantially unknownappearance.

FIG. 2 shows an example application of a system according to theinvention for tracking the motion of a web of continuous material.

FIG. 3 shows a block diagram of an illustrative apparatus.

FIG. 4 shows a flowchart describing an illustrative embodiment of amethod according to the invention.

FIG. 5 shows a static feature detection process for an illustrativeembodiment wherein the features to be detected are edges.

FIG. 6 shows a convolution kernel used in the static feature detectionprocess of FIG. 5.

FIG. 7 shows a method for determining the weight of a static edge usedin certain illustrative embodiments.

FIG. 8 shows a ring buffer used to store dynamic features in anillustrative embodiment wherein the objects are of substantially unknownappearance, and further shows one such dynamic feature.

FIG. 9 shows a snapshot of the operation of an illustrative embodiment,and corresponding static and dynamic edges.

FIG. 10 shows portions of a memory storing values associated with thestatic and dynamic edges of FIG. 9, as well as selected map data.

FIG. 11 shows a flowchart describing an illustrative embodiment of amethod for updating dynamic features for objects of substantiallyunknown appearance.

FIG. 12 shows a block diagram of an illustrative embodiment of a systemaccording to the invention that can be used, among many purposes, fortracking moving objects and material.

FIG. 13 shows a snapshot of the operation of an illustrative embodiment,including material of substantially unknown appearance, that usestwo-dimensional images and wherein the features are blobs detected byconnectivity analysis.

FIG. 14 shows static blob features corresponding to the object featuresof FIG. 13.

FIG. 15 shows dynamic blob features corresponding to the object featuresof FIG. 13.

FIG. 16 shows portions of a memory storing values associated with thestatic and dynamic edges of FIGS. 14 and 15, as well as a three degreeof freedom pose.

FIG. 17 shows an example training object for creating predetermineddynamic edges.

FIG. 18 shows a portion of a memory for storing a predetermined dynamicfeature.

FIG. 19 shows a portion of a flowchart describing an illustrativeembodiment of a method for detecting objects using predetermined dynamicfeatures.

FIG. 20 shows another portion of a flowchart describing an illustrativeembodiment of a method for detecting objects using predetermined dynamicfeatures.

FIG. 21 shows various elements used in an illustrative system and methodfor dynamic spatial calibration.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following detailed description of the illustrative embodiments,reference is made to the accompanying drawings which form a part hereof,and in which are shown by way of illustration specific embodiments inwhich the invention may be practiced. It is to be understood that otherembodiments may be utilized and structural changes may be made withoutdeparting from the scope of the invention.

SOME DEFINITIONS

As used herein a sensor is any device that can produce a digital image,including but not limited to optoelectronic devices, x-ray machines,scanning electron microscopes, ultrasonic devices, radar, and sonar.

As used herein the terms object and material are generallyinterchangeable and refer to any physical substance that can interactwith a sensor so as to produce a digital image. The interactiontypically involves some form of radiation, such as light, x-rays, ormicrowaves, but can also include sound or any other means for producingan image. Objects and material may be, for example, discrete items orcontinuous webs. In the descriptions and claims of this patent, one termor the other may be chosen for linguistic style, or one or both may beused for no particular reason, and these choices should not beinterpreted as limiting the description or claim to certain kinds ofphysical substances unless a more specific substance is explicitlycalled out.

An object feature is a physical feature on an object that, byinteraction with a sensor, gives rise to a pattern in a digital image.Object features have physical properties such as position and size. Animage feature is such a pattern, which can be analyzed to provideinformation about the object feature and the object that contains it.

A static feature comprises information extracted from an image featureby an image analysis method or system. A static edge is a type of staticfeature that may, for example, arise from a discontinuity of depth,orientation, or reflectance on the surface of an object. A static blobis a type of static feature that may, for example, arise from aconnected region of uniform depth, orientation, or reflectance. Staticedge and blob detection methods are well-known in the art. Staticfeatures can also be obtained using any of a wide variety of well-knownmethods, including for example matched filters, Hough transforms, andgeometric pattern matching.

A dynamic feature comprises information that describes and correspondsto an object feature, wherein the information is determined by analysisof a plurality of images captured during relative movement between theobject or material and the field of view. The information comprises atleast one measurement of a physical property of the object feature, forexample its position. Individual elements of the information are calledattributes or values. A dynamic edge is a type of dynamic feature thatmay, for example, correspond to a discontinuity of depth, orientation,or reflectance on the surface of an object. A dynamic blob is a type ofdynamic feature that may, for example, correspond to a connected regionof uniform depth, orientation, or reflectance. Dynamic features can alsoinclude, for example, holes, corners, lines, and patterns.

As used herein a process refers to systematic set of actions directed tosome purpose, carried out by any suitable apparatus, including but notlimited to a mechanism, device, component, module, software, orfirmware, or any combination thereof that work together in one locationor a variety of locations to carry out the intended actions, and whetheror not explicitly called a process herein. A system according to theinvention may include suitable processes, in addition to other elements.

The descriptions herein may be of embodiments of systems or methodsaccording to the invention, wherein such systems comprise variousprocesses and other elements, and such methods comprise various steps.It will be understood by one of ordinary skill in the art thatdescriptions of systems can easily be understood to describe methodsaccording to the invention, and visa versa, where the processes andother elements that comprise the systems would correspond to steps inthe methods.

Motion and Coordinate Systems

The invention uses motion of objects or material in and/or through thefield of view to capture and process a plurality of images thatcorrespond to a plurality of pixel grid alignments. Estimates of theposition, weight, and other attributes of a feature are based on ananalysis of the appearance of the feature as it moves relative to thefield of view and appears at a plurality of pixel grid alignments. Theresulting reliability and accuracy is superior to prior art staticfeature detection systems and methods.

According to an illustrative embodiment of the present invention,physical motion is described by one or more parameters corresponding toone or more degrees of freedom in which the motion may occur. Forexample, motion of rigid bodies in a 2D image might be described bythree parameters, two of translation and one of rotation. Similarly,motion of a rigid body in a 1D image would be described by onetranslation parameter. Motion closer to and/or farther away from certainkinds of sensors can be described by a size degree of freedom, since theobject would appear larger or smaller as it moved closer or farther. Fora 1D sensor of this kind, for example, motion could be described by twoparameters, one of translation and one of size. While motion isgenerally expected to be principally confined to the degrees of freedomof a particular embodiment, motion in other degrees of freedom can oftenbe tolerated without significant effect.

In general many other well-known degrees of freedom can be used asappropriate to the motion in a given application, including, forexample, those of the affine and perspective transformations.Furthermore, the objects need not be rigid bodies as long as theparameters of motion can be defined. The set of parameters used todefine the degrees of freedom in a particular embodiment are referred tocollectively as the pose of the object or material relative to the fieldof view, or conversely the pose of the field of view relative to theobject or material. The pose provides, for each image, a mapping betweenpoints on the object and points in the image.

While some motion is a necessary element of dynamic feature detection,the motion need not be continuous or smooth. Motion may change inmagnitude and/or direction arbitrarily and without any prior knowledgeor warning. Furthermore, objects may come to rest in the field of viewfor indefinite periods of time. In all cases, motion is understood to berelative motion between a sensor and an object. Illustrative embodimentsof the present invention may be described herein using a stationarysensor and a moving object. It should be noted that in alternativeembodiments, the sensor may be in motion while the object is stationaryand/or both the object and the sensor may be in motion. As such, thedescription of a stationary sensor and a moving object should be takenas exemplary only.

Dynamic features are associated with a moving object or material, notwith the field of view, and so their position and orientation attributesare advantageously represented in a coordinate system attached to theobject or material. This coordinate system, which may be one- ortwo-dimensional depending on the dimensionality of the images in use, isreferred to herein as object coordinates. The range of objectcoordinates may be unbounded in any or all directions. For example, inan illustrative 1D embodiment for use with a continuous web of material,object coordinates would comprise position along the web, and would havean unbounded range of at least the length of the web. In embodimentsusing 1D images, the object coordinate may be referred to herein asglobal position.

Static features detected by the systems and methods of the invention areassociated with the field of view, and are advantageously represented inimage coordinates. Given the unbounded range of object coordinates, itis convenient to use the pose of the field of view relative to theobject to provide a mapping between object and image coordinates. Notethat this mapping may be different for each captured image, as theobject or material moves in the various degrees of freedom of the pose.

Exemplary Applications

FIG. 1 illustrates an example application of an illustrative embodimentof the invention to detect and/or track objects. Conveyer 100 movesboxes 110, 112, 114, 116, and 118 in direction of motion 102. Each boxin this example includes a label, such as example label 120, and adecorative marking, such as example marking 122. A printer 130 printscharacters, such as example characters 132, on each label as it passesby. In the example of FIG. 1, the boxes are the objects to be detectedand tracked.

The illustrated embodiment comprises an apparatus 150 that outputsdetection signal 134 to printer 130 at times when labels to be printedpass, or are in some desirable position relative to, reference point106. In an illustrative embodiment detection signal 134 comprises apulse indicating that a label has been detected, and wherein the leadingedge of the pulse occurs at the time that a label is at reference point106 and thereby serves to indicate that printing should begin. Detectionsignal 134 is provided according to the invention using predetermineddynamic features that correspond to the known appearance of the boxes,as further described herein. Detection signal 134 may advantageously besynchronized with the location of the labels using systems and methodsdisclosed in the above-incorporated U.S. patent application Ser. Nos.10/865,155 and 11/763,752.

Apparatus 150 further outputs motion signal 136 to printer 130 toprovide information about the motion of the boxes (position and/orvelocity) for control of the printing process. In an illustrativeembodiment motion signal 136 comprises the well-known quadratureencoding. Motion signal 136 may be provided according to the inventionusing dynamic features that correspond to unknown object features of theboxes, as further described herein. Such unknown object features mayinclude edges of the labels or decorative markings, texture on thesurface of the boxes, or any other visually resolvable features.

The illustrative embodiment of FIG. 1 outputs both detection signal 134and motion signal 136 to printer 130. Many other arrangements may beused with alternative embodiments of the invention, including outputtingjust one of the signals, using two separate devices looking at twodistinct portions or surfaces of the boxes to output the two signals,and outputting one or both signals to an intermediary device such as aprogrammable controller.

FIG. 2 illustrates an example application of an illustrative embodimentof the invention to track a continuous web of material. Web 210 moves indirection of motion 220 relative to apparatus 230. Web 210 containsobject features in the form of surface microstructure that gives rise toimage features when illuminated by grazing illumination 250. Images areformed using telecentric lens 240 so that the optical magnification isconstant in the presence of fluctuations in distance between web 210 andapparatus 230. Use of grazing illumination and telecentric optics arewell-known in the art.

Apparatus 230 operates in accordance with the invention to output motionsignal 235, which in an illustrative embodiment is a quadrature signal.With the arrangement of FIG. 2, motion signal 235 provides informationabout the true motion of web 210, in contrast to the indirect and, attimes, inaccurate information that might be provided by a conventionalmechanical encoder attached to a drive shaft of the transport mechanism.

Illustrative Apparatus

FIG. 3 is a block diagram of an illustrative embodiment of a portion ofan apparatus according to the invention. Microcontroller 300, such asthe AT91SAM7S64 sold by Atmel Corporation of San Jose, Calif., comprisesARMv4T processor 310, read/write SRAM memory 320, read-only flash memory322, universal synchronous/asynchronous receiver transmitter (USART)330, and parallel I/O interface (PIO) 332. Illustratively, the processor310 controls the other elements of microcontroller 300 and executessoftware instructions 324 stored in either SRAM 320 or flash 322 for avariety of purposes. SRAM 320 holds working data 326, and flash 322holds non-volatile data 328 used by processor 310. Microcontroller 300operates at a clock frequency of 55 MHz. As will be appreciated by oneskilled in the art, other processors, storage media, peripherals,interfaces, and configurations of these elements may be used inalternative embodiments of the present invention. As such, thedescription of microcontroller 300 and its elements should be taken asexemplary only.

Linear optical sensor 340, such as the TSL3301-LF sold by Texas AdvancedOptoelectronic Solutions (TAOS) of Plano, Tex., comprises a linear arrayof 102 photoreceptors. Linear optical sensor 340, under control ofprocessor 310 by commands issued using USART 330, can expose the lineararray of photoreceptors to light for an adjustable period of time calledthe exposure interval, and can digitize the resulting 102 lightmeasurements and transmit them in digital form to USART 330 for storagein SRAM 320. Linear optical sensor 340, also under control of processor310, can apply an adjustable analog gain and offset to the lightmeasurements of each of three zones before being digitized, as describedin TAOS document TAOS0078, January 2006, the contents of which arehereby incorporated by reference.

In an illustrative embodiment, linear optical sensor 340 is calibratedto compensate for any non-uniformity of illumination, optics, andresponse of the individual photoreceptors. A uniformly white object isplaced in the field of view, or moved continuously in the field of view,and the gain for each of the three zones is set so that the brightestpixels are just below saturation. Then a calibration value is computedfor each pixel, such that when each gray level is multiplied by thecorresponding calibration value, a uniform image is produced for theuniformly white object. The calibration values are stored in flash 322and applied to subsequent captured images. The calibration values arelimited such that each gray level is multiplied by no more than 8. Eachimage used by the calibration procedure is obtained by averaging 512captured images.

Any suitable means can be employed to illuminate the field of view oflinear optical sensor 340. In an illustrative embodiment, two 630 nmLEDs are aimed at the field of view from one side of linear opticalsensor 340, and two 516 nm LEDs are aimed at the field of view from theother side. A light-shaping diffuser, such as those manufactured byLuminit of Torrance, Calif., is placed in front of the LEDs so thattheir beams are spread out parallel to linear optical sensor 340. Inanother illustrative embodiment, LEDs are placed to form grazingillumination suitable for imaging surface microstructure.

Human users, such as manufacturing technicians, can control the systemby means of human-machine interface (HMI) 350. In an illustrativeembodiment, HMI 350 comprises an arrangement of buttons and indicatorlights. Processor 310 controls HMI 350 using PIO interface 332. In otherembodiments, an HMI consists of a personal computer or like device; instill other embodiments, no HMI is used.

The apparatus of FIG. 3 produces signal 362 for purposes includingindicating detection and motion of objects. Signal 362 is connected toautomation equipment 360 as required by a given application. Signal 362can be generated by PIO interface 332, under control of processor 310,or by other peripheral devices contained in or external tomicrocontroller 300. Note that automation equipment 360 is shown forillustrative purposes only; signal 362 may be used for any purpose andneed not be connected to any form of automation equipment, and signal362 need not be used at all.

In an illustrative embodiment, various processes are carried out by aninteracting collection of digital hardware elements, including forexample those shown in the block diagram of FIG. 3, suitable softwareinstructions 324 residing in SRAM 320 or flash 322, working data 326residing in SRAM 320, and non-volatile data 326 residing in flash 322.

The illustrative embodiment of FIG. 3 is suitable for one-dimensionalimages. By replacing linear optical sensor 340 with a two-dimensionalsensor, an apparatus suitable for two-dimensional images is obtained.For such an apparatus it may be advantageous to also replacemicrocontroller 300 with a more powerful processor. For example, theapparatus taught in previously incorporated U.S. patent application Ser.No. 10/865,155 may be used.

Processes Employed by Various Embodiments of a System According to theInvention

As used herein a capture process obtains images of the field of view ofa sensor. The images may be in any form that conveys information aboutobjects and material in the field of view and is suitable for analysisby other processes as required. The images may reside in the sensor, inmemory external to the sensor, or any combination; and may be obtainedby any suitable form of analog and/or digital signal processing,including but not limited to gain and offset, resampling, change inresolution, time filtering, and/or spatial filtering.

In the illustrative apparatus of FIG. 3, the capture process comprisesdigitizing light measurements so as to obtain an array of numbers,called pixels, whose values, called gray levels, correspond to the lightmeasurements; transferring the pixels from linear optical sensor 340 tomicrocontroller 300; and storing the pixels into SRAM 320 for subsequentanalysis. Following the usual convention in the art, a pixel may refereither to an element of the array of numbers, or to a unit of distancecorresponding to the distance between two adjacent elements of thearray. The array of pixels in this illustrative apparatus is aone-dimensional digital image.

As used herein a motion process provides relative motion between objectsand the field of view of a sensor. The objects, the sensor, and/or thefield of view can be moving, as long as there is relative motion.Example motion processes include, but are not limited to: a conveyermoving objects past a fixed or moving sensor; a sensor attached to arobot arm that moves the sensor past fixed or moving objects; a fixedobject and a fixed sensor that uses some means, for example a movingmirror, to move the field of view; and objects in freefall that pass afixed or moving sensor.

As used herein a static feature detection process analyzes an image toproduce a set of static features. Such a set may contain any number ofstatic features, including none, depending on the content of the image.In the illustrative embodiment of FIG. 3, a static feature detectionprocess comprises a digital computation carried out by processor 310,operating on working data 326 stored in SRAM 320 or in the registers ofprocessor 310, under the control of software instructions 324 stored ineither SRAM 320 or flash 322.

As used herein an analysis process comprises a computation that operateson certain input information to produce certain result information, forexample the analysis process that determines a map. In the illustrativeembodiment of FIG. 3, an analysis process comprises a digitalcomputation carried out by processor 310, operating on working data 326stored in SRAM 320 or in the registers of processor 310, under thecontrol of software instructions 324 stored in either SRAM 320 or flash322.

Alternative embodiments of the invention may optionally include asignaling process whose purpose is to produce a signal that communicatesinformation obtained in the course of their operation, for exampleinformation about object motion, identity, and/or quality. A signal cantake any form known in the art, including but not limited to anelectrical pulse, an analog voltage or current, data transmitted on aserial, USB, or Ethernet line, radio or light transmission, and messagessent or function calls to software routines. The signal may be producedautonomously or in response to a request from other equipment.

In the illustrative embodiment of FIG. 3, a signaling process comprisesa digital computation carried out by processor 310, operating on workingdata 326 stored in SRAM 320 or in the registers of processor 310,producing signal 362 under the control of software instructions 324stored in either SRAM 320 or flash 322. Signal 362 may comprise anelectrical pulse, a quadrature encoding using two wires, or any suitablearrangement.

As will be appreciated by one skilled in the art, any combination ofprocesses that interact in any way is also a process. Thus thedescription of various embodiments as comprising various specificprocesses is made for convenience and ease of comprehension only. It isexpressly contemplated that in alternative embodiments similar functionsmay be performed by additional or fewer processes. As will further beappreciated by one skilled in the art, processes may be taught hereinwithout explicitly being called processes. As such, the descriptionherein of a specific set of processes should be taken as exemplary only.

An Illustrative Method

FIG. 4 shows a flowchart describing an illustrative embodiment of amethod according to the invention. The flowchart shows a continuous loopof steps that are carried out at some suitable rate. For theillustrative apparatus described below in relation to FIG. 3, forexample, the rate is around 8000 images per second. However, as will beappreciated by one skilled in the art, varying rates may be utilized inalternative embodiments. As such, the description of 8000 images persecond should be taken as exemplary only.

Capture step 400 captures an image of the field of view. Since the stepsof FIG. 4 form a continuous loop, the effect of capture step 400 is tocapture a sequence of images of the field of view.

Static detection step 410 detects a set of static features in thecaptured image, as further described herein. The set may have any numberof static features, including none, depending on the image.

Analysis step 420 analyzes the set of static features and a storedplurality of dynamic features (described in detail herein) to determinea map between the static and dynamic features. The map generally has twocomponents: a pose that maps between points on the object and points inthe image, and an association that maps dynamic features tocorresponding static features. Typically a dynamic feature correspondsto one static feature, but there may be ambiguous cases where a dynamicfeature corresponds to more than one static feature, as well as caseswhere a dynamic feature corresponds to no static feature. In addition toproducing a map, analysis step 420 may produce a match score thatindicates a degree of confidence that that map is valid, i.e. that theobject or material continues to be correctly tracked as it moves.

In an illustrative embodiment using one dimensional images and edges, astatic edge corresponds to a dynamic edge if the position of the staticedge, after being mapped to object coordinates using the pose, issufficiently close to the position of the dynamic edge, and further ifthe polarity of the edges match. In an illustrative 2D embodiment, therequirement that the polarities match is replaced by a requirement thatthe orientations, after suitable mapping to object coordinates, aresufficiently close. Other rules of correspondence can be used inalternative embodiments of the invention, including rules that ignorepolarity or orientation altogether, and rules that use other attributesof the features. In certain illustrative embodiments, static featuresthat correspond to no dynamic features are ignored; in otherillustrative embodiments, information from such static features may beused for a variety of purposes.

Update step 430 uses the map to updates dynamic features so as toimprove their measurements of physical properties of correspondingobject features, for example improving accuracy and/or reliability, asdescribed in more detail herein.

The illustrative embodiment of FIG. 4 produces information in the formof the dynamic features themselves, as well as the sequence of maps thatresult from analysis step 420. This information can be used to output awide variety of signals as may be appropriate for a given application.Output step 440, for example, outputs a motion signal that can be usedto indicate the position, velocity, or other measurement of the motionof the object.

Illustrative Static Feature Detection

FIG. 5 shows a static feature detection process in an illustrativeembodiment using static edges. Image 500 is processed by gradientestimator 510, resulting in estimates of image gradient at variouspoints in image 500. For a one-dimensional image, gradient is a signedscalar value; for a two-dimensional image, gradient is a vector.

Gradient estimates are processed by peak detector 520, which produces aset of preliminary static edges (not shown) corresponding to points inimage 500 where the magnitude of the gradient is a local maximum andexceeds detection threshold 560. Peak detector 520 also convertsgradient magnitude at such locations to a weight value using unitythreshold 562, as further described below in relation to FIG. 7.

The preliminary static edges, if any, are further processed by subpixelinterpolator 530 to produce the set of static edges 540. Subpixelinterpolator 530 uses parabolic interpolation to provide subpixel edgeprecision. Example static edge 550, suitable for an illustrativeembodiment using one-dimensional images, comprises values indicatingposition, polarity, and weight of the static edge. As previously noted,position is in image coordinates, and therefore relative to the field ofview.

FIG. 6 shows a convolution kernel 600 used by gradient estimator 510 inthe static feature detection process of FIG. 5 for an embodiment usingone-dimensional images. Convolution kernel 600 is a composition of afirst-difference operator and a 5-element approximately Gaussiansmoothing kernel. Note that the output resulting from convolution withconvolution kernel 600 can be divided by 8 for unity gain.

FIG. 7 shows a method for determining the weight of a static edge usedin certain illustrative embodiments, including the embodiment of FIG. 5.The method of FIG. 7 is suitable for one- or two-dimensional images, andthe principal behind it can be applied to any form of static featuredetection.

Shown is a graph 700 of weight 720 as a function 730 of gradientmagnitude 710. Weight 720 falls within the range 0.0-1.0 and indicatesrelative confidence that the static edge corresponds to an objectfeature and not, for example, some artifact of instrumentation. Peakdetector 520 insures that no static edges are produced having gradientmagnitude values below detection threshold 560, and so weightscorresponding to such values are effectively zero, as shown. Gradientmagnitudes at detection threshold 560 correspond to minimum weight 722,and those at unity threshold 562 and above correspond to weight 1.0.

In an illustrative embodiment following FIGS. 5, 6, and 7, and whereinimage 500 uses 8-bit pixels and convolution kernel 600 is divided by 8for unity gain, detection threshold 560 is 8, unity threshold 562 is 24,and minimum weight 722 is 0.33.

The use of weight is not a required element of the invention. Resultsidentical to those of embodiments that do not use weight can generallybe achieved by embodiments herein described by replacing function 730with a step function, for example by setting unity threshold 562 equalto detection threshold 560 so that weight 720 is 0.0 below detectionthreshold 560 and 1.0 at or above it. The illustrative embodiment ofFIG. 7, however, may result in a significant improvement in reliabilityover embodiments without weight, by turning detection threshold 560 froma hard threshold into a soft threshold. Instead of a static featuregoing from no influence to full influence as its weight crossesdetection threshold 560, it goes from no influence to small andincreasing influence as its weight rises to unity threshold 562.

As is well-known to one of ordinary skill in the art, the literature ofdigital image processing provides a wide variety of alternative methodsfor one- and two-dimensional static edge detection that can be used topractice the invention. As such, the description of static edgedetection described above in reference to FIGS. 5-7 should be taken asexemplary only.

Dynamic Feature Detection for Objects of Substantially UnknownAppearance

In some embodiments the objects or material in the field of view are ofsubstantially unknown appearance, i.e. the object features are not knownin advance. Dynamic features are created as motion brings new physicalfeatures or characteristics into the field of view, are updatedresponsive to a learning process over a plurality of images as theobjects or material move, and may optionally be discarded once themotion carries them substantially beyond the field of view.

FIG. 8 shows a memory used to store dynamic features in an illustrativeembodiment using one-dimensional images and used for objects andmaterial of substantially unknown appearance. The memory is organized asa ring buffer 800 comprising elements such as example element 810, eachof which holds one dynamic feature, such as example dynamic feature 802.In FIG. 8 unshaded elements such as empty element 812 are not currentlyin use.

Dynamic features in ring buffer 800 are sorted in order of increasingglobal position. Lower pointer 820 indicates the stored dynamic featurewith the lowest global position, which are located closest to one end ofthe field of view, and upper pointer 822 indicates the stored dynamicfeature with the highest global position, which are located closest tothe other end of the field of view. Since birth and death of dynamicedges typically occurs at the ends of the field of view, where motioncarries new features in and old features out, birth and death processescan be handled efficiently, and proper sorting can be maintained with noextra operations. Furthermore, keeping dynamic edges sorted by positionallows for very efficient pattern matching methods.

FIG. 9 shows a snapshot of the operation of an illustrative embodimentusing one-dimensional images and static and dynamic edges. Material 900moves in direction of motion 902 relative to field of view 920 marked byboundaries 922. Associated with field of view 920 are image coordinateaxis 926 and image origin 924. Material 900 contains object featuresmarked as shown, including first object feature 911, second objectfeature 912, third object feature 913, fifth object feature 915, seventhobject feature 917, and eighth object feature 918.

A static edge detection process produces a set of static edges for acaptured image corresponding to the indicated relative position ofmaterial 900 and field of view 920. Static edges are shown along imagecoordinate axis 926, including first static edge 931, second static edge932, fifth static edge 935, and ninth static edge 939. In the figurestatic edges are shown with length indicating weight and directionindicating polarity. First static edge 931 arises from first objectfeature 911, and likewise for the others.

Note that third object feature 913 gives rise to no static edge in thisimage, and of course neither do seventh object feature 917 and eighthobject feature 918 because those two are outside of field of view 920.Furthermore, ninth static edge 939 is an artifact and arises from nophysical object feature.

Dynamic edges in existence prior to any analysis of the current imageare shown along current object coordinate axis 940, and those inexistence after analysis of the current image are shown along nextobject coordinate axis 950. In the figure dynamic edges are shown withlength indicating experience and direction indicating polarity.

Next first dynamic edge 951 has just been born from first static edge931; it did not exist prior to analysis of the current image, and wascarried into field of view 920 by motion of material 900.

Current second dynamic edge 942 corresponds to second static edge 932,and is updated to produce next second dynamic edge 952, which has moreexperience and a more accurate position.

Current third dynamic edge 943 corresponds to no static edge, and isupdated to produce next third dynamic edge 953 by incrementing the age,but which gains no more experience.

Current fifth dynamic edge 945 corresponds ambiguously to fifth staticedge 935 and ninth static edge 939. In the illustrated embodiment, theambiguity is resolved in favor of fifth static edge 935 because it iscloser to current fifth dynamic edge 945. If two ambiguous static edgeshappened to be equally close, the one with the larger weight would bechosen. If the weights were also equal, their positions would beaveraged (the average position is of course the position of the dynamicedge, since they are equally close and cannot be at the same position).Note that while many other rules can be devised to handle ambiguouscorrespondence, it is desirable to avoid rules that choose the one withthe lowest (or highest) coordinate, because such a rule would introducea direction bias into measurements of pose that would accumulate overlong travel distances. Current fifth dynamic edge 945 is updated toproduce next fifth dynamic edge 955. Age and experience increase, butposition won't change significantly because current fifth dynamic edge945 is already quite experienced.

Current seventh dynamic edge 947 is now outside field of view 920. It isupdated to produce next seventh dynamic edge 947 by incrementing the agebut not otherwise changing. It continues to live for now because it isstill close to field of view 920.

Current eighth dynamic edge 948 is now sufficiently outside field ofview 920 that it dies.

FIG. 10 shows a first memory 1000 storing values associated with thestatic and dynamic edges of FIG. 9, as well as selected map data. FIG.10 also shows second memory 1050 storing further map data as well ascertain values computed by an illustrative analysis that determines themap.

Static edge column 1010 is a portion of first memory 1000 containingpolarity, weight, and position values corresponding to the static edgesof FIG. 9. Current dynamic edge column 1012 is a portion of first memory1000 containing polarity, position, age, and experience valuescorresponding to the current dynamic edges of FIG. 9. Next dynamic edgecolumn 1016 is a portion of first memory 1000 containing position, age,and experience values corresponding to the next dynamic edges of FIG. 9(polarity values are the same as for current dynamic edge column 1012).Map data column 1014 is a portion of first memory 1000 containingselected map data, specifically weight, force, and offset.

In the illustrative embodiment of FIGS. 9 and 10, pose has one degree offreedom, represented by the global position (i.e. object coordinate) ofimage origin 924. Part of the analysis that determines the map in theillustrated embodiment computes, as further described below, coarseposition 1052, which is an approximate pose. Coarse position 1052 isused by a fine alignment process to determine the map, i.e. the pose(global position of image origin 924) and association (static edges towhich the current dynamic edges correspond), as will now be described.

As shown in second row 1022, static edge column 1010, second static edge932 has positive polarity, weight 0.90, and position 14.20 pixels inimage coordinates. Using coarse position 1052 second static edge 932maps to global position 14.20+3715.00=3729.20. As further shown insecond row 1022, current dynamic edge column 1012, second static edge932 is determined to correspond to current second dynamic edge 942because the polarities match and because they are sufficiently close(within 1.0 pixels in this illustrative embodiment)—current seconddynamic edge 942 at global position 3728.70 is only −0.50 pixelsdistant.

Referring now to second row 1022, map data column 1014, thecorrespondence between second static edge 932 and current second dynamicedge 942 is given a weight of 2.60 because that is experience of currentsecond dynamic edge 942. It is further given a force of −0.50, whichmeans that the evidence provided by this correspondence suggests thatfield of view 920 should be “pulled” by −0.50 pixels from coarseposition 1052 to come into better alignment with material 900. Of courseas previously noted static edges are of limited accuracy andreliability, so this is just evidence and is not decisive.

These concepts of force and evidence used in this illustrativeembodiment are a one-dimensional, one degree of freedom specializationof those concepts as presented in, for example, U.S. Pat. No. 6,658,145,entitled FAST HIGH-ACCURACY MULTI-DIMENSIONAL PATTERN INSPECTION, thecontents of which are hereby incorporated by reference. The teachingstherein can be used in embodiments of the present invention that usetwo-dimensional images and multiple degrees of freedom.

Fourth row 1024 and sixth row 1026 are similar to second row 1022.

Third row 1023 shows that current third dynamic edge 943 corresponds tono static edge, and so gets a correspondence weight of 0.00 and is givenno force. Similarly, seventh row 1027 and eighth row 1028 show thatcurrent seventh dynamic edge 947 and current eighth dynamic edge 948also correspond to no static edges, in this case because they are beyondfield of view 920.

Fifth row 1025 and ninth row 1029 show that two static edges correspondto current fifth dynamic edge 945, fifth static edge 935 and ninthstatic edge 939. Both static edges match the polarity of current fifthdynamic edge 945, and both are sufficiently close (−0.60 and +0.90pixels). As noted above, the ambiguity is resolved in favor of fifthstatic edge 935 because it is closer, and this correspondence is givenweight 9.00 and force −0.60.

Common offset 1054 is −0.32, which is the weighted average of the forcesweighted by the map weights of map data column 1014, which as describedabove are the experiences of dynamic edges that have correspondingstatic edges. This means that, considering all the evidence and trustingmore experienced dynamic edges to a greater extent, field of view 920should be pulled by −0.32 pixels from coarse position 1052 to come intosubstantially best alignment with material 900. Thus the analysis thatdetermines the map produces global position 1056 (i.e. pose in thisillustrative embodiment) of 3714.68. It has also, in the course of theanalysis, produced an association mapping dynamic edges to correspondingstatic edges.

In an alternative embodiment using one dimensional images and a twodegree of freedom pose comprising a position and a size degree offreedom, the fine alignment process computes a weighted linearregression between the image coordinate values of the static edges foundin static edge column 1010 and the object coordinate values of thedynamic edges found in current dynamic edge column 1012, weighted by thecorrespondence weights (which come from dynamic edge experience) foundin map data column 1014. The weighted linear regression for the exampleof FIG. 10 gives y=1.0004x+3714.67, where x is an image coordinate and yis an object coordinate. This provides not only a global position value(3714.67) but also a size value (1.0004) that indicates a 0.04% relativedifference between the size of features in the field of view and thesize of features in the image. The size degree of freedom can, dependingon the nature of the sensor, be interpreted as motion of the objectcloser to or farther away from the sensor.

Match score 1058 is a measure of confidence that the analysis hasdetermined a valid map, i.e. that material 900 continues to be tracked.It is computed in this illustrative embodiment by summing thecorrespondence weights in map data column 1014 and then dividing by thesum of the experiences of the current dynamic edges that are withinfield of view 920, which can be found in current dynamic edge column1012. Essentially this is the weighted faction of dynamic edges still inthe field of view that correspond to at least one static edge, againtrusting more experienced dynamic edges to a greater extent. In thisexample match score 1058 is 0.89, which is considered to be sufficientto trust the map determined by the analysis.

As long as coarse position 1052 is reasonably close to the position ofsubstantially best alignment, the fine alignment process will produce anaccurate and reliable map that does not significantly depend on theexact value of coarse position 1052. If coarse position 1052 is too faraway, fine alignment will fail. There is typically an intermediate rangeof coarse position where the fine alignment will move the pose closer tothe position of substantially best alignment, but not get the best map.In such a case repeating the fine alignment one or more times, forexample as described in the above-incorporated U.S. Pat. No. 6,658,145,can be advantageous (U.S. Pat. No. 6,658,145 describes these finealignment steps as attract steps). In an illustrative embodiment, finealignment is always run twice. In another illustrative embodiment, finealignment is run a second time only if the first run produced a matchscore below some threshold, for example 0.85.

Next dynamic edge column 1016 shows how the map is used to update thedynamic edges in this illustrative embodiment. As shown in eighth row1028, current eighth dynamic edge 948 dies and is deleted from memory1000. As shown in third row 1023 and seventh row 1027, current thirddynamic edge 943 and current seventh dynamic edge 947 gain one unit ofage but gain no experience because they had no corresponding staticedges. Their global position also doesn't change, which can be seen byinspecting current dynamic edge column 1014 and next dynamic edge column1016.

As shown in second row 1022, fourth row 1024, fifth row 1025, and sixthrow 1026, current second dynamic edge 942, current fourth dynamic edge944, current fifth dynamic edge 945, and current sixth dynamic edge 946gain one unit of age and also gain experience equal to the weight of thestatic edge to which they correspond (in the case of current fifthdynamic edge 945, after resolving the ambiguity).

The map is now used to improve the global positions of the dynamic edgesthat correspond to at least one static edge. This is in effect alearning process, wherein the less experienced dynamic edges learn theirtrue global position from the more experienced ones. Referring to mapdata column 1014, each dynamic edge that corresponds to at least onestatic edge is given an offset value that is the difference between theglobal position of the static edge (i.e. its image coordinate mapped toobject coordinate by the pose) and the dynamic edge. Note that thissimilar to the force, but based on global position 1056 instead ofcoarse position 1052 and of opposite sign.

Dynamic edge position is learned in this illustrative embodimentaccording to the formula:

${{next}\mspace{14mu} {position}} = {{{current}\mspace{14mu} {position}} + \frac{{static}\mspace{14mu} {weight} \times {offset}}{{next}\mspace{14mu} {experience}}}$

Notably, since the updated experience (next experience in the formula)is the sum of all of the corresponding static weights over the life ofthe dynamic edge, the formula gives a weighted average position of allthose static edges, after mapping to object coordinates using the poses.

One advantageous feature of this illustrative embodiment of the presentinvention is that learning diminishes as experience grows. Since theoffsets are based on the pose, which is more influenced by moreexperienced dynamic edges, and learning generally decreases asexperience increases, knowledge is passed from more experienced to lessexperienced dynamic features.

As shown in first row 1021, next first dynamic edge 951 is born fromfirst static edge 931. Its age is 1, its position is determined bymapping the image position of first static edge 931 to objectcoordinates using the pose (i.e. global position 1056), and itsexperience is the static weight of first static edge 931.

As will be appreciated by one skilled in the art, many variations on theillustrative embodiment of FIGS. 9 and 10 can be envisioned withoutdeparting from the spirit of the invention. In one such variation,updating ends completely when a dynamic edge reaches a predeterminedage, for example 64.

FIG. 11 shows a flowchart describing an illustrative embodiment of amethod for updating dynamic features for objects of substantiallyunknown appearance. In this embodiment FIG. 11 is a more detaileddescription of update step 430 of FIG. 4.

Death step 1110 deletes dynamic features beyond a certain “point ofdeath” that is advantageously set somewhat beyond the ends of the fieldof view, for example 1 pixel beyond. If the direction of motionreverses, dynamic edges that are outside the field of view but that havenot yet reached the point of death can come back into the field of viewand bring with them all of their experience.

Accept test step 1120 examines the match score to decide if the map canbe trusted. If it is at or above a certain accept threshold, for example0.40, the map is trusted.

If the map is trusted, motion test step 1130 examines the pose to see ifthere has been any motion since the last capture image. The inventionrecognizes that it is generally desirable to avoid updating dynamicfeatures that correspond to objects at rest, because the rest positionrepresents an accidental alignment and the statistics of that alignmentwould soon swamp those obtained while the object is in motion. Theinvention further recognizes that in order to avoid the measurementnoise of stationary objects being interpreted as motion, it is generallydesirable to use some hysteresis, for example 1.5 pixels, in detectingthe rest condition. If motion test step 1130 detects some motion, themap is used to update the dynamic features, for example as describedabove. If not, birth step 1170 is entered as described below.

If the map is not trusted, flush test step 1150 examines the match scoreto see if tracking has failed completely. If it is at or above a certainflush threshold, for example 0.20, tracking may recover on the nextimage, i.e. a trusted map may be produced. If flush test step 1150judges that tracking has failed completely, restart step 1160 deletesall dynamic edges so a fresh start can be made.

Birth step 1170 creates new dynamic features from static featuresfollowing any of a variety of suitable rules. In an illustrativeembodiment, new dynamic edges are born from static edges that correspondto no living dynamic edges, are located at positions that are lower orhigher than all living dynamic edges by some margin, for example 2.5pixels, and have weights that exceed a certain birth weight, for example0.90. Note that if all dynamic edges were deleted by restart step 1160there will be no living dynamic edges when birth step 1170 runs, so awhole new set can be created.

It is clear that many variations on the illustrative embodiment of FIG.11 can be envisioned without departing from the spirit of the invention.In one such variation, motion test step 1130 is moved to the beginning,so that birth or death only occur of there has been motion. Thisvariation may be more efficient, because if there is no motion thennothing is moving into or out of the field of view and so there is nochance of birth or death. However, FIG. 11 illustrates that the primaryadvantage of using motion test step 1130 is to avoid contaminating thelearning with an accidental alignment.

Illustrative System for Distance and Speed Measurement of Material ofSubstantially Unknown Appearance

FIG. 12 shows a block diagram of an illustrative embodiment of a systemaccording to the invention that can be used, among many purposes, fortracking moving objects and material and producing distance and/or speedsignals.

Static features 1200 are detected as previously described and used byother elements of FIG. 12 as shown and further described below. Dynamicfeatures 1240 are stored in a memory, are born, live, and die inresponse to life cycle process 1230, and are used by other elements ofFIG. 12, all as previously described and further described below. Lifecycle process 1230 comprises a birth process, life process (also calleda learning process herein), and a death process, all as illustrativelydescribed herein.

Capture time 1202 is produced by the image capture process and indicatesa time at which each frame was captured, for example the center of theexposure interval. Time is used in this illustrative embodiment tocompute velocity and also as part of analysis process 1210 that computesmap and score 1220. It is important to understand, however, that time isnot a required element of dynamic feature detection, and is used only incertain embodiments where, for example, a velocity measure is desired.The motion that is essential to dynamic edge detection refers to asequence of positions of an object or material relative to a field ofview, where neither the timing of that sequence nor the velocity of thatmotion are important.

Differentiator 1250 uses capture time 1202 and map and score 1220 toproduce an instantaneous measurement of velocity for each capturedimage. If the score is sufficient to trust the map, the instantaneousvelocity is determined by dividing the change in one or more degrees offreedom of the pose by the change in capture time, both changes beingmeasured from some previous image, typically the most recent. If thescore is not sufficient to trust the map (tracking temporarily lost),instantaneous velocity, in this illustrative embodiment, is zero. Notethat the degrees of freedom of velocity are a subset (including possiblyall) of the degrees of freedom of the pose. For example, if poseincludes two degrees of freedom of position and one of orientation,velocity can include up to two linear and one angular components.

Instantaneous velocity is filtered by low pass filter 1252, for examplea two-pole, critically damped, infinite impulse response digitallow-pass filter, to produce velocity measurement 1260. The time constantof low pass filter 1252 can be set to filter out velocity variations(often just measurement noise) that are significantly faster than themechanical time constants of the objects or material in use, resultingin very accurate measurements. Furthermore, in this illustrativeembodiment, when tracking is lost velocity measurement 1260 is at firstheld at the last known value, and then decays to zero over time ascontrolled by the settable time constant. As will be seen in thefollowing further description of FIG. 12, this insures that a brief lossof tracking has little significant effect on the system, but that in thepresence of a prolonged loss velocity measurement 1260 approaches zero.Of course a tracking loss is not the same as the object coming to rest;in the latter case, tracking is maintained.

Analysis process 1210 uses static features 1200 and dynamic features1240 to determine map and score 1220, which is used to update dynamicfeatures 1240 in response to life cycle process 1230. Life cycle process1230 can operate, for example, as described in relation to FIGS. 9, 10,and 11. Analysis process 1210 includes fine alignment process 1216 thatcan operate, for example, as described in relation to FIG. 10, usingstatic features 1200, dynamic features 1240, and coarse pose 1218 todetermine map and score 1220. Note that coarse position 1052 of FIG. 10is an example of a coarse pose 1218 that might be used by fine alignmentprocess 1216.

An illustrative process for determining coarse pose 1218 will now bedescribed. It is desirable that such a process represent a good tradeoffamong speed, accuracy, and reliability. One simple process for coarsealignment would be to run the fine alignment using a number of coarseposes spaced over some range, and choose the one with the highest matchscore. Such a process would generally be accurate and reliable, butmight be too slow in many applications. The process of FIG. 12 isusually faster than such a simple process.

Prediction process 1212 uses capture time 1202, pose 1222 (which holdsthe pose computed from the last captured image), and velocitymeasurement 1260 to predict the pose of the current image assumingconstant velocity. Accelerations with time constants shorter than thetime constant of low pass filter 1252 lead to prediction errors, andwhile more sophisticated prediction processes can be devised, these aregenerally neither necessary nor sufficient. To be reliable one must atsome point look at the actual image, and that is the role of coarsealignment process 1214. Prediction process 1212 is an inexpensive way togive coarse alignment process 1214 a hint, but in many embodiments itcan be dispensed with entirely.

Many methods of coarse alignment are known. For example, methods taughtin U.S. Pat. No. 7,016,539, entitled METHOD FOR FAST, ROBUST,MULTI-DIMENSIONAL PATTERN RECOGNITION, the contents of which are herebyincorporated by reference, are well-suited to two-dimensional imageswith many degrees of freedom of pose. In an illustrative embodimentusing one-dimensional images, such as that described in relation toFIGS. 9 and 10, a 1D version of the well-known generalized Houghtransform is used. Coarse alignment process 1214 considers a range ofposes that is centered on the predicted pose and extends far enough tohandle any expected prediction errors, for example ±1.25 pixels, but notso far that speed of operation is unacceptably low. If for some reasoncoarse alignment is judged to fail (e.g. there is no clear andunambiguous position of best alignment), the predicted pose is usedinstead for coarse pose 1218.

If the score is not sufficient to trust the map (tracking temporarilylost), pose is generally undefined. In the illustrative embodiment ofFIG. 12, pose 1222 is set from the map if it is trustworthy, otherwiseit is set to the predicted position. Thus if tracking is briefly lost,low pass filter 1252 insures that pose 1222 continues to update based onthe last known velocity, ramping down to stationary after some time.

Signaling process 1270 uses pose 1222 and/or velocity measurement 1260to output signals 1272 that can be used by other systems to track theposition and/or velocity of the object or material. A quadrature signalcan be used for position, for example.

Illustrative Embodiment Using 2D Images and Blob Features

FIGS. 13-16 show an illustrative embodiment using 2D images and blobfeatures. Referring to FIG. 13, material 1300 of substantially unknownappearance moves relative to 2D field of view 1310 and contains objectfeatures including first object feature 1320, second object feature1330, and third object feature 1340.

FIG. 14 shows the result of static feature detection using well-knownconnectivity analysis (also called blob analysis) to detect first staticblob 1420, arising from first object feature 1320, and second staticblob 1430, arising from second object feature 1330. In this illustrativeembodiment the static feature detector rejects static features that fallon the edge of field of view 1310, and so no static blob is detectedarising from third object feature 1340. Of course no static blobs can bedetected from objects features that lie outside field of view 1310.

Static blobs in this illustrative embodiment have a center of mass,shown as a dot in the center of the blob, which includes two degrees offreedom of position. Static blobs also have area, shown as a boundingoval, and an angle of the principal axis, shown as an arrow extendingfrom the center of mass. Static blobs can also have a weight, computedfor example from area, contrast relative to the background, and/or anyother suitable attribute. If weight is not used it can be considered tobe 1.0 for all static blobs.

FIG. 15 shows dynamic blobs, including first dynamic blob 1520,corresponding to first object feature 1320, second dynamic blob 1530,corresponding to second object feature 1330, and third dynamic blob1540, corresponding to third object feature 1340. Second dynamic blob1530 has just entered field of view 1310 and been born from secondstatic blob 1430. Third dynamic blob 1540 is beyond the point of deathand is about to die.

FIG. 16 shows portions of a memory storing values associated with theillustrative dynamic blob detection embodiment. First memory portion1600 holds a three degree of freedom pose, comprising two degrees offreedom of position (x,y) and one of orientation (θ). Second memoryportion 1610 holds a static blob, comprising center of mass, area, andangle of the principal axis. Third memory portion 1620 holds a dynamicblob, comprising center of mass, area, angle of the principal axis, age,experience, and variability.

Dynamic Feature Detection Using Predetermined Dynamic Features

In alternative embodiments a predetermined set of dynamic features isused, corresponding to objects or material of substantially knownappearance. The predetermined set of dynamic features may be obtainedfrom a training process that is responsive to a training object; from aCAD or mathematical description; or from any suitable process. For suchembodiments, the dynamic feature attributes may include both expectedand measured values. The expected attributes may come from predeterminedinformation, while the measured attributes may be initialized wheneveran object substantially similar in appearance to the known appearanceenters the field of view, and be updated as the object moves. Suchembodiments may further include in the set of dynamic feature attributesage and variability. Objects substantially similar in appearance to theknown appearance are called herein detectable objects.

FIG. 17 shows example training object 1700 for creating predetermineddynamic features for use in detecting objects substantially similar inappearance to training object 1700. A training object can be used, forexample, in an application such as that shown in FIG. 1 where detectionsignal 134 is to be produced for use by a printer for printing, forexample, expiration date 1720. Of course the date would be printed onthe objects substantially similar in appearance to training object 1700,and not necessarily on training object 1700 itself.

In an illustrative embodiment, training on a training object isaccomplished by using a dynamic feature detection embodiment designed tooperate on unknown objects or material. Of course the training object ormaterial is unknown until such time as it becomes known by the trainingprocess itself. In this embodiment, dynamic feature detection on unknownobjects or material is run as the training object is moved into thefield of view. A signal is received to complete the training process byexamining the dynamic features in existence at the time of the signal,choosing those whose attributes indicate reliability, and creating thepredetermined set of dynamic features to be used for dynamic featuredetection on objects substantially similar in appearance to the trainingobject. The training object may be at rest or in motion at the time ofthe signal.

Reliability may be indicated by average weight, position variability, orany other suitable process or method that will occur to one of ordinaryskill in the art. In an illustrative embodiment, a dynamic feature ischosen to be included in the predetermined set of dynamic features ifits attributes indicate that it had a corresponding static feature in atleast 90% of the images during its lifetime.

In FIG. 17 field of view 1730 is shown in a position relative totraining object 1700 at the time of the signal. First dynamic edge 1740,of negative polarity, second dynamic edge 1742, of positive polarity,and third dynamic edge 1744, of negative polarity, exist at the time ofthe signal. Note that while these dynamic edges are shows as withinfield of view 1730, this is clearly not a requirement, and dynamic edgeswell beyond field of view 1730 can advantageously be used.

FIG. 18 shows a memory portion 1800 for storing a predetermined dynamicedge in an illustrative embodiment.

Polarity 1810, expected position 1820, and expected weight 1830 are thepredetermined attributes of the dynamic edge. They are determined inadvance, for example using a training process, and generally are notupdated by, for example, update step 430 of FIG. 4. If a trainingprocess using a dynamic feature detection embodiment designed to operateon objects or material of substantially unknown appearance is used, forexample, polarity 1810 and expected position 1820 can be taken directlyfrom a dynamic edge in existence at the time of the training signal.Expected position 1820 can, if desired, be adjusted to be relative tosome arbitrary image coordinate system, since its object coordinate atthe time of the training signal is generally of no furtherinterest—usually only relative positions of the predetermined dynamicedges matter. Expected weight 1830 can be calculated from a dynamic edgein existence at the time of the training signal by taking the ratio ofexperience to age.

Measured position 1840, measured weight 1850, age 1860, experience 1870,and variability 1880 are initialized and updated as further describedbelow.

FIGS. 19 and 20 show a flowchart describing an illustrative embodimentof a method for detecting objects using predetermined dynamic features.The illustrated method has two major states: inactive, when there isinsufficient evidence that a detectable object is in or passing throughthe field of view, and active, when there is some evidence that adetectable object may be in or passing through the field of view. Beingin the active state does not necessarily mean that a detectable objectis indeed present, but means at least that one might be and that furtherevidence may be needed.

FIG. 19 shows the portion of the flowchart for the inactive state.Capture step 1910 captures an image of the field of view, and staticdetection step 1920 detects a set of static features in the image, bothas previously described.

Inactive coarse alignment step 1930 attempts to find a rough pose atwhich at least some, for example half, of the predetermined dynamicfeatures have corresponding static features. In other words, inactivecoarse alignment step 1930 is looking for some evidence that adetectable object has entered the field of view. In an illustrativeembodiment using one dimensional images and edge features, a 1D versionof the well-known generalized Hough transform is used. Typically it canbe assumed that a detectable object will first appear near an edge ofthe field of view, but since the velocity may be unknown a relativelywide range of poses near the edges is considered, for example 10 pixels.

Inactive found test step 1940 examines the results of inactive coarsealignment step 1930 and decides whether there is some evidence that adetectable object has entered the field of view. If not, the inactivestate continues at capture step 1910. If so, initialization step 1950initializes all of the non-predetermined attributes of the dynamicfeatures. In the illustrative embodiment of FIG. 18, for example, thenon-predetermined attributes are measured position 1840, measured weight1850, age 1860, experience 1870, and variability 1880, all of which areinitialized to zero. The active state is then entered at active terminal2000.

FIG. 20 shows the portion of the flowchart for the active state. Capturestep 2010 captures an image of the field of view, and static detectionstep 2020 detects a set of static features in the image, both aspreviously described.

Analysis step 2030 analyzes the static and dynamic features to determinea map, and comprises active coarse alignment step 2032 and finealignment step 2034. Prediction is not used in this illustrativeembodiment, although it could be if desired. Active coarse alignmentstep 2032 is similar to inactive coarse alignment step 1930, except thata generally narrower range of poses is considered, for example ±2 pixelscentered on the pose of the previous captured image instead of nearedges of the field of view.

Fine alignment step 2034 is similar to fine alignment as previouslydescribed, where the position of a dynamic edge is expected position1820 and the weight is taken from experience 1870 as usual.Alternatively, weight for the fine alignment step can be taken fromexpected weight 1830, or can be ignored (set to 1.0). Fine alignmentstep 2034 also computes a match score as previously described.

Note that when the active state is entered at active terminal 2000,inactive coarse alignment step 1930 has already been run and so activecoarse alignment step 2032 can be skipped for this image.

Active found test step 2040 examines the match score to decide if themap can be trusted. If it is at or above a certain accept threshold, forexample 0.50, the map is trusted.

A count is kept of consecutive images for which the map was found not tobe trustworthy. The count is reset to zero whenever a trustworthy map isfound, and incremented otherwise. Lost test step 2080 examines thiscount to see if the count has reached some limit, for example 3. If notthe active state continues with the next image at capture step 2010; ifthe limit is reached it is concluded that there is no longer sufficientevidence that a detectable object is in or passing through the field ofview. It may be that a detectable object has now passed completelythrough the field of view, or that one was never really there in thefirst place. Object test step 2090 examines the most recent detect score(computed as described below) to judge between these two cases. If thedetect score is at or above some detect threshold, for example 0.25,signal step 2092 outputs a detect signal indicating that an object wasdetected. In either case, the inactive state is entered at inactiveterminal 1900.

In an alternative illustrative embodiment, the detect signal is outputusing the synchronization methods taught in U.S. patent application Ser.No. 10/865,155 and/or U.S. patent application Ser. No. 11/763,752, sothat it can be used to precisely locate the object. The signal in thisembodiment might be output before or after the inactive state is enteredat inactive terminal 1900.

The count of consecutive images for which the map was found not to betrustworthy can also be used to increase the range of poses consideredby active coarse alignment step 2032, because it may be desirable tolook in a wider range when tracking is lost in one or more previousimages. For example, the range can be increased by ±1 pixel for everymissed image, then reset back to its normal value once tracking isregained.

If active found test step 2040 concludes that the map can be trusted,update step 2050 uses the map to update the dynamic features. Age 1860and experience 1870 are updated as previously described. Measuredposition 1840 is expected position 1820 plus the weighted average offsetof all of the static edges that were found to correspond to the dynamicedge (offsets are further described in relation to FIG. 10 and map datacolumn 1014). Measured weight 1850 is the ratio of experience to age.Variability 1880 is the standard deviation of the offsets of all of thestatic edges that were found to correspond to the dynamic edge. A weightvariability can also be computed as the standard deviation of theweights of all those static edges. As will be appreciated by one skilledin the art, other ways of updating age, experience, measured position,measured weight, and variability may be used, and different and/oradditional attributes may be used in dynamic edges.

In some embodiments for which objects or material of substantially knownappearance are used, it may be desirable to compute a detect score inaddition to the match score. A detect score provides a measure ofconfidence that a detectable object is seen, which may be different thanthe match score, whose primary purpose is to provide a measure ofconfidence in the map. For example, it may be desirable to recognize andbegin to track an object when it is only partially within the field ofview, before enough of it is seen to be sure that it is in fact anobject of the known appearance. At such times it is desirable to have ameasure of confidence in the map (the match score), but to reservejudgment on the identity of the object (detect score) until more isseen.

With such a scheme it is possible, for example, to use predetermineddynamic features that cover a much larger region of the object than thefield of view. The match score is used to assess the reliability of themap as portions of the object that fit in the field of view are tracked.The detect score is derived from the state of the dynamic features andis updated as the object moves and more is learned about the appearanceof the object.

In the illustrative embodiment of FIG. 20, detect score step 2060computes a detect score from the state of the dynamic features. Thedetect score is equal to the smallest measured weight 1850 over alldynamic features that have passed sufficiently within the field of view,for example at least 4 pixels from an edge, and are of sufficient age,for example 8, to have reliable attributes. The effect of such a methodof computing a detect score is to require that there be some evidencethat all of the predetermined dynamic features that should be visible(because the object or material has moved sufficiently far into thefield of view) are in fact visible. Note that a pattern matching methodbased on a prior art static feature detector that requires evidence thatall expected features are found would rarely be practical, because itstoo likely that a feature would be weak or missing in any given image.But with the dynamic feature detector of the present invention, a veryweak feature that may be missing from most of the images may indeed showup in the right position in at least some images, providing thenecessary evidence that it exists.

Detect test step 2070 examines the detect score to decide if there issufficient evidence that a detectable object is in or passing throughthe field of view. As long as all dynamic features that have passedsufficiently within the field of view and are of sufficient age havemeasured weight 1850 at or above some detect threshold, for example0.25, the active state continues at capture step 2010. If it is decidedthat there is no longer sufficient evidence that a detectable object isin or passing through the field of view, the inactive state is enteredat inactive terminal 1900.

Dynamic Spatial Calibration

In many embodiments it is desirable that the pose that maps betweenpoints on the object and points in the field of view be a lineartransformation. It may be, however, that the true map between object andimage space is non-linear due to a variety of fixed factors, includingbut not limited to lens distortion, non-orthogonal viewing angle, andcurved object or material surfaces. Any or all of these factors maycombine to make the true map non-linear. For example, a continuous webof material may be drawn over a portion of the curved surface of aroller, and may additionally be viewed using a lens with some geometricdistortion.

It is well-known in the art to use a spatial calibration process tocorrect for fixed non-linear effects, so that the pose can be linear andaccurate. In such a process, a calibration object containing features ofa known and precise geometric arrangement is placed in the field of viewof a sensor. A static feature detector determines the image coordinatesof the features, and uses those measurements and the known objectgeometry to construct a calibration, for example a piecewise lineartable that maps between image and object coordinates.

While a traditional static spatial calibration process can be used tocorrect fixed non-linear effects in embodiments of the presentinvention, such a process is limited by the accuracy of the staticfeature detector. Following the teachings herein, a dynamic spatialcalibration method and system uses motion to achieve higher accuracy. Afurther advantage of the dynamic calibration method and system is thatit allows using a calibration object that need not be as precisely madeas for a static calibration process.

FIG. 21 shows various elements used in an illustrative system and methodfor dynamic spatial calibration of one-dimensional images. A calibrationobject containing calibration features 2100 moves in field of view 2110of a sensor. Calibration features 2100 are, in this illustrativeembodiment, approximately evenly-spaced edges. Small uncorrelated errorsin the position of individual edges tend to cancel out during dynamiccalibration, so the calibration object can be made inexpensively simplyby printing the pattern on a piece of paper using a dot-matrix or laserprinter. The motion relative to the field of view can be, for example,in one direction or back and forth, and can be uniform or random. In anillustrative embodiment, back and forth motion by hand is used.

A sequence of images of field of view 2110 is captured, and a staticedge detection process, for example as described in relation to FIGS.5-7, is run on each one. FIG. 21 shows the processing of one such image.Static edges are shown along field of view 2110, including examplepositive static edge 2120 and example negative static edge 2122.

In the following, x will represent image coordinates and y willrepresent object coordinates. Let the (x, y) coordinates of center point2112 be (0, 0).

Static edges are given an index i, numbered left to right starting with0, so that example positive static edge 2120 has i=0 and examplenegative static edge 2122 has i=11. Since the spacing of the staticedges is uniform in object space, y_(i)=k(i−p), where k is an arbitraryconstant and p is the “index” of center point 2112. Their imagecoordinate x_(i) comes from the static edge detection process, and is ingeneral non-uniform.

For each image, p is determined. This is accomplished in theillustrative embodiment by considering the static edges that fall withincentral zone 2114 of the field of view. Center point 2112 and centralzone 2114 are chosen so that the mapping from image to object space incentral zone 2114 is reasonably linear. Typically lens distortion isminimal near the center, for example, and in general one can achievesufficient linearity by making the zone smaller. In an illustrativeembodiment using the apparatus of FIG. 3, central zone 2114 is 20pixels.

If fewer than a certain number of edges, for example 2, is present incentral zone 2114 the image is rejected and the calibration processcontinues with the next image. Otherwise a linear regression of the formx=ai+b using the static edges in central zone 2114 is used to determinep, which is the value of i in the regression where x=0. Once p is known,the (x_(i), y_(i)) coordinates of all static edges are known.

Since p changes as the calibration object moves relative to field ofview 2110, the value of p can be used to determine whether thecalibration object has moved since the last image. Following thereasoning described above in relation to motion test step 1130 of FIG.11, if no motion is detected (again using some hysteresis) the image isrejected and the calibration process continues with the next image.

Field of view 2110 is divided into a number of evenly spaced bins, forexample one bin per image pixel. A temporary memory 2130 holds linearregression data for each bin, including first example bin 2132 andsecond example bin 2134. Every static edge falls into a certain bin, andhas an image space offset u_(i) within that bin. In FIG. 21, examplepositive static edge 2120 falls into first example bin 2132 with offset2150, and example negative static edge 2122 falls into second examplebin 2134 with offset 2152. For each static edge, the linear regressiondata for the bin into which it falls is updated using a linearregression of the form y_(i)=au_(i)+b.

Once a sufficient number of images have been processed, the regressiondata in each bin are used to determine the object coordinate of thecenter, or alternatively the left or right end, of each bin. Thoseobject coordinates are saved in piecewise linear calibration table 2140,after which temporary memory 2130 can be deleted. Piecewise linearcalibration table 2140 can be used to transform the position of staticfeatures to values that are linear in object space for any embodiment ofthe invention that uses one dimensional images.

Embodiments of dynamic spatial calibration for two dimensional imagesand/or features other than edges can be devised by one of ordinary skillin the art. For example, dynamic calibration using two dimensionalimages and blob features replaces calibration features 2100 with atwo-dimensional array of approximately evenly spaced blob features,central zone 2114 with a two-dimensional region in the image, temporarymemory 2130 with a two-dimensional array of regression data, piecewiselinear calibration table 2140 with a two-dimensional bi-linear table,and other changes that will be apparent to one of ordinary skill in theart.

For calibration features 2100, if the dark lines are of a differentwidth than the light spaces between them then the positive edges will beapproximately evenly spaced, and the negative edges will beapproximately evenly spaced, but the edge-to-edge spacing will not beeven. In an alternative embodiment suitable for such a case, positiveand negative polarity static edges are processed separately for eachimage.

The foregoing has been a detailed description of various embodiments ofthe invention. It is expressly contemplated that a wide range ofmodifications, omissions, and additions in form and detail can be madehereto without departing from the spirit and scope of this invention.For example, the processors and computing devices herein are exemplaryand a variety of processors and computers, both standalone anddistributed can be employed to perform computations herein. Likewise,the linear array sensor described herein is exemplary and improved ordiffering components can be employed within the teachings of thisinvention. Also, some, or all, of the capture process, analysis process,and optional signaling process can be combined or parsed differently.Numerical constants used herein pertain to illustrative embodiments; anyother suitable values or methods for carrying out the desired operationscan be used within the scope of the invention. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

1-22. (canceled)
 23. A computer program product for optically measuringa plurality of object features of an object in motion relative to aone-dimensional field of view, such that the object passes through theone-dimensional field of view, the product tangibly embodied in anon-transitory computer readable medium, the computer program productcomprising instructions being operable to cause a data processingapparatus to: receive data for a sequence of images of theone-dimensional field of view; detect static features acrosssubstantially all of the sequence of images of the one-dimensional fieldof view; receive, from a memory, a plurality of dynamic features basedon the plurality of object features, the plurality of dynamic featurescomprising at least one measurement of a physical property of thecorresponding object feature; calculate a plurality of maps, wherein atleast one of the plurality of maps calculated for an image of thesequence of images is based on the static features of the image and thecorresponding dynamic features of the plurality of dynamic features forthe image, the map comprising a pose that maps between at least a pointon the object and at least a point in the image; and an association thatmaps the corresponding dynamic features to at least some of the staticfeatures of the image so to produce associated features; and compute atleast one updated dynamic feature based on the corresponding map,whereby improving at least one measurement of a physical property of anobject feature.
 24. The product of claim 23, wherein the object is ofsubstantially unknown appearance and the product further comprisesinstructions being operable to cause the data processing apparatus tocompute and store in the memory new members of the plurality of dynamicfeatures in response to static features evidencing the relative motionbetween the object and the one-dimensional field of view.
 25. Theproduct of claim 24, wherein each of the dynamic features furthercomprise an experience factor; and wherein the instructions beingoperable to cause the data processing apparatus to: calculate theplurality of maps further comprises increasing the influence of at leastone corresponding dynamic feature on the map as the experience factor ofthe at least one corresponding dynamic feature increases; and whereinthe instructions being operable to cause the data processing apparatusto compute the at least one updated dynamic feature further comprisesdecreasing the influence of the map on the at least one dynamic featureas the experience factor of the at least one dynamic feature increases.26. The product of claim 25, wherein the experience factor is responsiveto a count of the images that were captured since the dynamic feature ofthe plurality of dynamic features was created.
 27. The product of claim25, wherein each static feature comprises a weight indicating relativeconfidence that the static feature is responsive to an object feature;and the experience factor is responsive to the weights of the staticfeatures to which the dynamic feature corresponds.
 28. The product ofclaim 24, further comprising instructions being operable to cause thedata processing apparatus to remove a dynamic feature of the pluralityof dynamic features from the memory in response to the static featuresevidencing relative motion between the object and the field of view. 29.The product of claim 23, wherein the object is of substantially knownappearance and the stored plurality of dynamic features arepredetermined responsive to the known appearance.
 30. The product ofclaim 29, wherein the computer program product further comprisesinstructions being operable to cause the data processing apparatus tocreate the predetermined plurality of dynamic features responsive to atraining object in the field of view of the received images.
 31. Theproduct of claim 30, wherein the instructions being operable to causethe data processing apparatus to create the predetermined plurality ofdynamic features further comprises receiving data of a second sequenceof images of a training object in the one-dimensional field of view, thetraining object in relative motion with the field of view; detectingtraining static features in the second sequence of images; receiving,from a training memory, training dynamic features based on the pluralityof training object features, the training dynamic features comprising atleast one measurement of a physical property of a training objectfeature; calculating a plurality of training maps, one map of theplurality of training maps for one image of the second sequence ofimages based on the training static features of the one training imageand the corresponding training dynamic features of the plurality oftraining dynamic features for the training image, the training mapcomprising a training pose that maps between points on the trainingobject and points in the corresponding image; and a training associationthat maps the corresponding training dynamic features to at least someof the training static features of the image; and computing an updatefor at least one training dynamic feature based on the correspondingtraining map; and selecting the predetermined plurality of dynamicfeatures from the plurality of training dynamic features.
 32. Theproduct of claim 33, further comprising instructions being operable tocause the data processing apparatus to calculate at least one scoreindicating relative confidence that an object substantially similar inappearance to the known appearance is in the field of view; and detectthe presence or absence of an object substantially similar in appearanceto the known appearance based on the at least one score.
 33. The productof claim 23, wherein the at least one updated dynamic feature comprisesan updated position of the at least one dynamic feature physicalproperty.
 34. The product of claim 33, wherein the updated position ofat least one dynamic feature varies without regard to relative distancebetween the at least one dynamic feature and a next dynamic feature ofthe plurality of dynamic features.
 35. The product of claim 23, whereinthe data of the sequence of images comprises capture times of thesequence of images, and the product further comprises instructions beingoperable to cause the data processing apparatus to calculate a velocityby dividing a change of position of the object between two images of thesequence of images by the difference in capture times of the two images;calculate a coarse position of the object in a subsequent image usingthe velocity and the capture time of the subsequent image.
 36. Theproduct of claim 23, wherein the static features and the plurality ofdynamic features comprise an edge position and an edge polarity.
 37. Theproduct of any of claims 23, 27, 29 and 32, further comprisinginstructions being operable to cause the data processing apparatus toprovide a value for consumption by an input/output module comprisinginformation about the motion of the object, the motion signal responsiveto at least one degree of freedom of a pose of the object.